For the specific setting of the educationally managed social media community, the task is simplified considering the reduced content variety compared to the external community. Also, while a CWB-RS must be aware of the condition and behavior of the entire community, this may be factored in terms of dynamic models of its members. Using different combinations of the same members models, it could be possible to create different community models that allow a broader set of training conditions for the CWB-RS in simulation. They will also enable online simulations for estimating the results of a sequence of recommendations (see Figure REF  and [1]}, [2]}). The literature on interaction models for social media is extensive.
[3]} was one of the first to show the importance of cognitive and content factors. The models proposed in [4]}, [5]} reason simultaneously on the patterns of propagation and the topics.
Most of these models do not account for user adaptation, which is crucial in this context. However, generative models based on adaptive paradigms, such as RL [6]}, [7]} and inverse RL [8]}, are useful to model users' behavior changes over time based on past interactions.
While these studies and many more led to improved forecasting systems, there is a consensus that there are intrinsic problems that limit the predictive power with both sufficient accuracy and anticipation, see for example [9]}. A significant improvement of base-line algorithms, requires very detailed information about the community [10]}. However, the CWB-RS has access to rich information about the educationally managed network. This, together with its limited, size will improve the efficacy of the predictive models.
